Assessment of School Assessors’ Knowledge and Competence on Diagnostic Overshadowing for Appropriate Placement of Children with Intellectual Disability in Cross River State, Nigeria
Bibliographic record
Abstract
This study assessed the knowledge and competence of special teachers and psychologists in diagnostic overshadowing and differential diagnosis in children with intellectual disability in Cross River State, Nigeria. A descriptive research design was adopted. Sixty (60) respondents comprising teachers and school psychologists were purposively selected from three main special schools in Cross River State. Two research questions were raised to guide the study. A rating scale titled ‘Mental Health Diagnosis Competency Scale (r=0.91)’ was used for data collection. The instrument was used to assess the knowledge and competence of special teachers and school psychologists in diagnostic overshadowing and differential diagnosis in children with intellectual disabilities. The data collected were statistically analyzed using percentages, frequency count, and bar chart. The findings revealed that most teachers and school psychologists have no knowledge of psychiatric symptomatology in children with intellectual disabilities. Respondents also lack adequate competence in differential diagnosis, leading to wrong special education placement and inadequate intervention plans for such children. It was recommended, among others, that the government provide in-service training for teachers and psychologists to equip them on current issues and practices in special education, such as differential diagnosis and collaborative partnership within a transdisciplinary approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".